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增强现实场景下移动边缘计算资源分配优化方法.docx

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增强现实场景下移动边缘计算资源分配优化方法 Title:OptimizationofResourceAllocationinEdgeComputingforAugmentedRealityApplications Abstract: Asaugmentedreality(AR)technologiescontinuetoevolve,thereisagrowingneedtoefficientlyallocatecomputingresourcesinedgecomputingenvironmentstosupportARapplications'real-timeprocessingrequirements.ThispaperproposesanoptimizationmethodforresourceallocationinedgecomputingtoenhancetheperformanceanduserexperienceofARapplications.Theobjectiveistominimizelatencyandmaximizethroughputwhileconsideringtheconstraintsoflimitedcomputationalpowerandnetworkbandwidthinedgedevices.TheproposedapproachleveragesmachinelearningalgorithmsanddynamicresourcemanagementtechniquestoachieveefficientresourceallocationinARscenarios. 1.Introduction Withtherapiddevelopmentofaugmentedreality(AR),real-timeprocessingandlow-latencycommunicationarecriticalfordeliveringseamlessARexperiences.However,client-serverarchitecturesorcloud-basedcomputingmodelsoftenintroducehighnetworklatencyanddependencyonnetworkstability,underminingthepotentialofARapplications.Toaddressthesechallenges,edgecomputingemergesasapromisingparadigmthatbringscomputationclosertothedatasource,reducinglatencyandimprovingresponsiveness.ThispaperfocusesonresourceallocationoptimizationinedgecomputingforARscenarios. 2.ResourceAllocationChallengesinEdgeComputingforAR InanARenvironment,mobiledevicesactastheendpointforARexperiences,capturingandrenderingdigitalcontentinreal-time.However,thesedevicestypicallyhavelimitedcomputationalpowerandrelyonedgeresourcestooffloadprocessingtasks.Thechallengesinresourceallocationincludedynamicallyprovisioningcomputingresources,offloadingcomputationtothemostsuitableedgeservers,optimizingnetworkcommunication,andadaptingtodynamicapplicationrequirements. 3.ProposedOptimizationMethod Theproposedoptimizationmethodconsistsofthefollowingsteps: 3.1.ResourceDemandPrediction:MachinelearningalgorithmsareemployedtopredictthecomputationalresourcedemandsofARapplicationsbasedonhistoricalusagepatterns,applicationchar